# updated at 2022.9.7 
import torch
from torch import nn
from d2l import torch as d2l

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

num_inputs, num_outputs, num_hiddens = 784, 10, 128

W1 = nn.Parameter(
    torch.randn(num_inputs, num_hiddens, requires_grad=True)
    )
b1 = nn.Parameter(torch.zeros(num_hiddens, requires_grad=True))
W2 = nn.Parameter(
    torch.randn(num_hiddens, num_outputs, requires_grad=True)
    )
b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))

params = [W1, b1, W2, b2]
loss = nn.CrossEntropyLoss()

def relu(X):
    a = torch.zeros_like(X)
    return torch.max(X, a)

def net(X):
    X = X.reshape((-1, num_inputs))
    H = relu(X @ W1 + b1)
    return (H @ W2 + b2)

if __name__ == "__main__":
    num_epochs, lr = 10, 0.1
    updater = torch.optim.SGD(params, lr=lr)
    d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)